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@ARTICLE{Valente_IEEE-SIGNALPROCESSINGLETTERS_2009,
         author = {Valente, Fabio},
       projects = {Idiap},
          month = {7},
          title = {A Novel Criterion for Classifiers Combination in Multistream Speech Recognition},
        journal = {IEEE Signal Processing Letters},
         volume = {16},
         number = {7},
           year = {2009},
           issn = {1070-9908},
            doi = {10.1109/LSP.2009.2019779},
       abstract = {In this paper we propose a novel information theoretic criterion for optimizing  the linear combination of  classifiers  in multi stream automatic speech recognition. We discuss an objective function that achieves a  trade-off between the minimization of a bound on the Bayes probability of error and the minimization of  the  divergence between the individual classifier outputs and their combination. The  method is compared with the conventional inverse entropy and minimum entropy combinations on both small and large vocabulary automatic speech recognition tasks. Results reveal that it outperforms  other linear combination rules. Furthermore we discuss the advantages of the proposed approach and the extension to other (non-linear) combination rules.},
            pdf = {https://publications.idiap.ch/attachments/papers/2009/Valente_IEEE-SIGNALPROCESSINGLETTERS_2009.pdf}
}